Australian Digital Technologies LeadersJason Zagami
Australian Digital Technologies Leaders
Presentation by Dr Jason Zagami to the Australian Digital Technologies Leaders (EdTechSA) on 13 April 2014 in Adelaide, SA.
Digital Technologies: What now?
Presentation by Dr Jason Zagami to the Queensland Studies Authority: Australian Curriculum conference on 22 March 2014 in Brisbane, QLD.
Teaching the Technologies learning area using a thinking skills approachJason Zagami
This document outlines an approach to teaching digital technologies and design and technologies using thinking skills such as systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It discusses each of these thinking skills in detail and provides examples of how they can be applied across the curriculum areas of digital technologies and design and technologies. The overall approach is to engage students in challenge-based learning projects that focus on solving complex problems using various thinking skills and collaborative processes.
The document discusses applying computational thinking in education. It defines several types of thinking including systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It then lists some big problems facing the world like global warming, food scarcity, and overpopulation. The document goes on to define key aspects of computational thinking including systems thinking, abstraction, data and information systems, algorithms and programming, digital systems, and implications and impacts. It provides examples of how computational thinking can be brought into the classroom through activities and projects involving things like Bee Bots, guessing games, computer games, mobile apps, websites, robotics, interfaces, wearables, and expert systems.
Lecture 4 Teaching Futures, Systems and Strategic Thinking 2016Jason Zagami
The document provides an overview of different types of thinking that can be taught, including systems thinking, computational thinking, design thinking, futures thinking, strategic thinking, and solutions thinking. It then focuses on futures thinking, outlining why studying the future is important and some tools used in futures thinking like environmental scans, trend analysis, scenarios, and backcasting. Finally, it discusses systems thinking and key concepts like stocks, flows, feedback loops, causal loops, and system dynamics modeling. The document aims to introduce various thinking approaches and tools that can be taught to help students develop important skills for understanding complex systems and creating preferred futures.
Keynote 1: Teaching and Learning Computational Thinking at ScaleCITE
Computational thinking involves problem formulation, pattern recognition, abstraction, and algorithm design. It is an important 21st century skill and countries are incorporating it into curricula. MOOCs can effectively deliver computational thinking education at scale. HKUST offers MOOCs on Java programming, app development, and engineering design that teach computational thinking concepts. Learning analytics provide insights into how students learn from MOOCs.
This document discusses teaching computational thinking through technologies education. It emphasizes developing students' thinking skills like design thinking, computational thinking, systems thinking and futures thinking through project-based learning. The document outlines curriculum outcomes, contexts, challenges and expectations for developing solutions across different year levels. It also discusses integrating different models of thinking, evaluating solutions, and the importance of creativity, innovation and accepting failure in the learning process.
Technology as human endeavour & Systems ThinkingJason Zagami
This document discusses technology and systems thinking. It provides an overview of technological evolution from the tool age to the digital revolution. It includes quotes about technology exceeding humanity and the importance of understanding science and technology. The document then discusses key aspects of systems thinking, including that a system is more than the sum of its parts, the importance of interconnections and feedback loops. It provides examples of how systems thinking can help analyze complex issues like pest control. Finally, it discusses the importance of mental models and simulations in systems thinking.
Australian Digital Technologies LeadersJason Zagami
Australian Digital Technologies Leaders
Presentation by Dr Jason Zagami to the Australian Digital Technologies Leaders (EdTechSA) on 13 April 2014 in Adelaide, SA.
Digital Technologies: What now?
Presentation by Dr Jason Zagami to the Queensland Studies Authority: Australian Curriculum conference on 22 March 2014 in Brisbane, QLD.
Teaching the Technologies learning area using a thinking skills approachJason Zagami
This document outlines an approach to teaching digital technologies and design and technologies using thinking skills such as systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It discusses each of these thinking skills in detail and provides examples of how they can be applied across the curriculum areas of digital technologies and design and technologies. The overall approach is to engage students in challenge-based learning projects that focus on solving complex problems using various thinking skills and collaborative processes.
The document discusses applying computational thinking in education. It defines several types of thinking including systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It then lists some big problems facing the world like global warming, food scarcity, and overpopulation. The document goes on to define key aspects of computational thinking including systems thinking, abstraction, data and information systems, algorithms and programming, digital systems, and implications and impacts. It provides examples of how computational thinking can be brought into the classroom through activities and projects involving things like Bee Bots, guessing games, computer games, mobile apps, websites, robotics, interfaces, wearables, and expert systems.
Lecture 4 Teaching Futures, Systems and Strategic Thinking 2016Jason Zagami
The document provides an overview of different types of thinking that can be taught, including systems thinking, computational thinking, design thinking, futures thinking, strategic thinking, and solutions thinking. It then focuses on futures thinking, outlining why studying the future is important and some tools used in futures thinking like environmental scans, trend analysis, scenarios, and backcasting. Finally, it discusses systems thinking and key concepts like stocks, flows, feedback loops, causal loops, and system dynamics modeling. The document aims to introduce various thinking approaches and tools that can be taught to help students develop important skills for understanding complex systems and creating preferred futures.
Keynote 1: Teaching and Learning Computational Thinking at ScaleCITE
Computational thinking involves problem formulation, pattern recognition, abstraction, and algorithm design. It is an important 21st century skill and countries are incorporating it into curricula. MOOCs can effectively deliver computational thinking education at scale. HKUST offers MOOCs on Java programming, app development, and engineering design that teach computational thinking concepts. Learning analytics provide insights into how students learn from MOOCs.
This document discusses teaching computational thinking through technologies education. It emphasizes developing students' thinking skills like design thinking, computational thinking, systems thinking and futures thinking through project-based learning. The document outlines curriculum outcomes, contexts, challenges and expectations for developing solutions across different year levels. It also discusses integrating different models of thinking, evaluating solutions, and the importance of creativity, innovation and accepting failure in the learning process.
Technology as human endeavour & Systems ThinkingJason Zagami
This document discusses technology and systems thinking. It provides an overview of technological evolution from the tool age to the digital revolution. It includes quotes about technology exceeding humanity and the importance of understanding science and technology. The document then discusses key aspects of systems thinking, including that a system is more than the sum of its parts, the importance of interconnections and feedback loops. It provides examples of how systems thinking can help analyze complex issues like pest control. Finally, it discusses the importance of mental models and simulations in systems thinking.
The document discusses analyzing the effects of computation. It provides examples of how computation is used in everyday objects like cell phones to transmit voice data and route calls. It emphasizes that computation involves collecting, storing, and processing data. The document also discusses how computation has allowed technologies like radio to enable personal communication through devices like cell phones. It states that analyzing computation involves observing how data is transformed and what is accomplished through that processing.
The document discusses a Digital Technologies curriculum that will be implemented in Victorian schools in 2015. It includes:
- Achievement standards that can be reached independently or embedded in other learning areas.
- Key concepts like abstraction, data collection and representation, algorithms, and digital systems that are the building blocks of the curriculum.
- Content descriptors at each year level that provide guidance on what to teach.
It also discusses computational thinking as applying an understanding of how computers work to solve problems, and three approaches schools can take to implement the new curriculum by examining current practice, new approaches, or building on existing knowledge. Assessment focuses on student products, processes, and evidence collected over multiple opportunities.
Jeannette M. Wing argues that computational thinking will be a fundamental skill used by everyone globally by mid-21st century, just like reading, writing and arithmetic. She defines computational thinking as involving abstraction, automation, and problem-solving approaches from computer science. Wing provides examples of computational thinking in various disciplines and calls for reforming curricula to teach computational thinking concepts from K-12 through graduate levels.
Full day lectures @International University, HCM City, Vietnam, May 2019. Review of AI in 2019; outlook into the future; empirical research in AI; introduction to AI research at Deakin University
In this video from the MIT Deep Learning Series, Lex Fridman presents: Deep Learning State of the Art (2020).
"This lecture is on the most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series."
Watch the video: https://wp.me/p3RLHQ-lng
Learn more: https://deeplearning.mit.edu/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Jane Hsu is a professor and department chair of Computer Science and Information Engineering at National Taiwan University. Her research interests include multi-agent systems, intelligent data analysis, commonsense knowledge, and context-aware computing. Prof. Hsu is the director of the Intel-NTU Connected Context Computing Center, featuring global research collaboration among NTU, Intel, and the National Science Council of Taiwan. She serves on the editorial board of Journal of Information Science and Engineering (2010-), International Journal of Service Oriented Computing and Applications (Springer, 2007-2009) and Intelligent Data Analysis (Elsevier/IOS Press, 1997-2002). She is actively involved in many key international AI conferences as organizers and members of the program committee. In addition to serving as the President of Taiwanese Association for Artificial Intelligence (2013-2014), Prof. Hsu has been a member of AAAI, IEEE, ACM, Phi Tau Phi, and an executive committee member of the IEEE Technical Committee on E-Commerce (2000) and TAAI (2004-current).
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
The document provides background information on the development of a K-12 computer science framework. It describes the framework's vision and principles, including empowering students to be informed citizens and understand computing's role in the world. The framework will outline computer science concepts and practices for different grade levels. Feedback will be gathered from reviewers and incorporated to improve the framework, which is intended to inform the development of state standards.
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
This document discusses the use of technology in psychology. It describes how virtual reality, computers, the internet and devices that integrate persuasive and ubiquitous computing can be used as therapeutic tools. Virtual reality can improve exposure therapy techniques. Computerized treatments have been shown to be effective for various disorders. The internet provides flexibility but also risks to privacy and confidentiality. The document also discusses experiences using these technologies in the Dominican Republic and Spain.
Machine Learning: Applications, Process and TechniquesRui Pedro Paiva
Machine learning can be applied across many domains such as business, entertainment, medicine, and software engineering. The document outlines the machine learning process which includes data collection, feature extraction, model learning, and evaluation. It also provides examples of machine learning applications in various domains, such as using decision trees to make credit decisions in business, classifying emotions in music for playlist generation in entertainment, and detecting heart murmurs from audio data in medicine.
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
Plenary address in reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014)
Conference Problem@Web | 2-4 May 2014 | Portugal
What happens when we teach a computer how to learn? Pablo shared surprising developments in the fast-paced field of Deep Learning, a family of techniques that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. We took a look at a field that will change the way the computers around us behave… sooner that we probably think. Pablo showed how Future Processing wants to play its role in helping doctors worldwide in their fight against cancer, through the ECONIB project.
The document summarizes a presentation given by Musstanser Tinauli on their research activities and experiments. It discusses their goals of understanding how interactive environments can be measured and how tools influence user behavior. It describes ongoing case studies of games, e-learning platforms and digital pens. It outlines their methodological approach and provides results from studies on a digital pen and paper system, including lessons learned. Recent publications and collaborations are also mentioned.
Artificial Intelligence - The greatest educational challenge ever?Marco Neves
1) The document discusses challenges and opportunities around AI in education, including how AI may impact the purpose of education and what skills should be taught as machines can perform more tasks.
2) It provides an overview of AI concepts and history, definitions of intelligence, and examples of current and emerging AI technologies.
3) The author proposes a framework for integrating AI into education, including teaching students about AI, learning with AI tools, and learning skills like ethics, collaboration and critical thinking that complement AI.
4) Classroom project ideas are suggested to introduce students to AI concepts in a hands-on way through tools, platforms and curriculum resources.
Lecture 2 Teaching Digital Technologies 2016Jason Zagami
This document provides an overview of key concepts related to teaching digital technologies, including computational thinking, systems thinking, design thinking, and futures thinking. It discusses important problems in the world like global warming, armed conflicts, and overpopulation that could be addressed through computational thinking. The document also outlines key concepts for different year levels, including creating interactive games, databases, and computer systems. It provides examples of concepts like algorithms, binary search, and the travelling salesman problem.
This document discusses key concepts related to computational thinking and systems thinking. It covers abstraction, data collection and representation, algorithms, specification, and implementation. Digital systems including hardware, software, and networks are explored. Interactions between people and digital systems and various impacts are also examined. The goal is for students to develop computational thinking skills to solve problems through project-based learning.
The document discusses analyzing the effects of computation. It provides examples of how computation is used in everyday objects like cell phones to transmit voice data and route calls. It emphasizes that computation involves collecting, storing, and processing data. The document also discusses how computation has allowed technologies like radio to enable personal communication through devices like cell phones. It states that analyzing computation involves observing how data is transformed and what is accomplished through that processing.
The document discusses a Digital Technologies curriculum that will be implemented in Victorian schools in 2015. It includes:
- Achievement standards that can be reached independently or embedded in other learning areas.
- Key concepts like abstraction, data collection and representation, algorithms, and digital systems that are the building blocks of the curriculum.
- Content descriptors at each year level that provide guidance on what to teach.
It also discusses computational thinking as applying an understanding of how computers work to solve problems, and three approaches schools can take to implement the new curriculum by examining current practice, new approaches, or building on existing knowledge. Assessment focuses on student products, processes, and evidence collected over multiple opportunities.
Jeannette M. Wing argues that computational thinking will be a fundamental skill used by everyone globally by mid-21st century, just like reading, writing and arithmetic. She defines computational thinking as involving abstraction, automation, and problem-solving approaches from computer science. Wing provides examples of computational thinking in various disciplines and calls for reforming curricula to teach computational thinking concepts from K-12 through graduate levels.
Full day lectures @International University, HCM City, Vietnam, May 2019. Review of AI in 2019; outlook into the future; empirical research in AI; introduction to AI research at Deakin University
In this video from the MIT Deep Learning Series, Lex Fridman presents: Deep Learning State of the Art (2020).
"This lecture is on the most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series."
Watch the video: https://wp.me/p3RLHQ-lng
Learn more: https://deeplearning.mit.edu/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Jane Hsu is a professor and department chair of Computer Science and Information Engineering at National Taiwan University. Her research interests include multi-agent systems, intelligent data analysis, commonsense knowledge, and context-aware computing. Prof. Hsu is the director of the Intel-NTU Connected Context Computing Center, featuring global research collaboration among NTU, Intel, and the National Science Council of Taiwan. She serves on the editorial board of Journal of Information Science and Engineering (2010-), International Journal of Service Oriented Computing and Applications (Springer, 2007-2009) and Intelligent Data Analysis (Elsevier/IOS Press, 1997-2002). She is actively involved in many key international AI conferences as organizers and members of the program committee. In addition to serving as the President of Taiwanese Association for Artificial Intelligence (2013-2014), Prof. Hsu has been a member of AAAI, IEEE, ACM, Phi Tau Phi, and an executive committee member of the IEEE Technical Committee on E-Commerce (2000) and TAAI (2004-current).
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
The document provides background information on the development of a K-12 computer science framework. It describes the framework's vision and principles, including empowering students to be informed citizens and understand computing's role in the world. The framework will outline computer science concepts and practices for different grade levels. Feedback will be gathered from reviewers and incorporated to improve the framework, which is intended to inform the development of state standards.
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
This document discusses the use of technology in psychology. It describes how virtual reality, computers, the internet and devices that integrate persuasive and ubiquitous computing can be used as therapeutic tools. Virtual reality can improve exposure therapy techniques. Computerized treatments have been shown to be effective for various disorders. The internet provides flexibility but also risks to privacy and confidentiality. The document also discusses experiences using these technologies in the Dominican Republic and Spain.
Machine Learning: Applications, Process and TechniquesRui Pedro Paiva
Machine learning can be applied across many domains such as business, entertainment, medicine, and software engineering. The document outlines the machine learning process which includes data collection, feature extraction, model learning, and evaluation. It also provides examples of machine learning applications in various domains, such as using decision trees to make credit decisions in business, classifying emotions in music for playlist generation in entertainment, and detecting heart murmurs from audio data in medicine.
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
Plenary address in reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014)
Conference Problem@Web | 2-4 May 2014 | Portugal
What happens when we teach a computer how to learn? Pablo shared surprising developments in the fast-paced field of Deep Learning, a family of techniques that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. We took a look at a field that will change the way the computers around us behave… sooner that we probably think. Pablo showed how Future Processing wants to play its role in helping doctors worldwide in their fight against cancer, through the ECONIB project.
The document summarizes a presentation given by Musstanser Tinauli on their research activities and experiments. It discusses their goals of understanding how interactive environments can be measured and how tools influence user behavior. It describes ongoing case studies of games, e-learning platforms and digital pens. It outlines their methodological approach and provides results from studies on a digital pen and paper system, including lessons learned. Recent publications and collaborations are also mentioned.
Artificial Intelligence - The greatest educational challenge ever?Marco Neves
1) The document discusses challenges and opportunities around AI in education, including how AI may impact the purpose of education and what skills should be taught as machines can perform more tasks.
2) It provides an overview of AI concepts and history, definitions of intelligence, and examples of current and emerging AI technologies.
3) The author proposes a framework for integrating AI into education, including teaching students about AI, learning with AI tools, and learning skills like ethics, collaboration and critical thinking that complement AI.
4) Classroom project ideas are suggested to introduce students to AI concepts in a hands-on way through tools, platforms and curriculum resources.
Lecture 2 Teaching Digital Technologies 2016Jason Zagami
This document provides an overview of key concepts related to teaching digital technologies, including computational thinking, systems thinking, design thinking, and futures thinking. It discusses important problems in the world like global warming, armed conflicts, and overpopulation that could be addressed through computational thinking. The document also outlines key concepts for different year levels, including creating interactive games, databases, and computer systems. It provides examples of concepts like algorithms, binary search, and the travelling salesman problem.
This document discusses key concepts related to computational thinking and systems thinking. It covers abstraction, data collection and representation, algorithms, specification, and implementation. Digital systems including hardware, software, and networks are explored. Interactions between people and digital systems and various impacts are also examined. The goal is for students to develop computational thinking skills to solve problems through project-based learning.
Presentation by Dr Jason Zagami to the Information Communication Technology Educators New South Wales (ICTENSW) conference on 15 March 2014 in Sydney, NSW.
Inclusive Futures for Europe. Beyond the Impacts of Industry 4.0 and Digital ...BEYOND4.0
The document discusses a project analyzing future skills needed for digital transformation in Europe. It outlines the project's goals of understanding future skills demands from employers, implications for vocational education, and opportunities for inclusiveness. The project aims to create a framework for classifying new skills and better skills data. It then presents the project's conceptualization of skills categorization, with groups like digital skills, professional skills, and analogous skills like complex thinking, social skills, and self-management. Within each category are examples of specific skills identified as important for future work.
Report on the National Agenda: ACARA to the Queensland Society for Information Technology in Education (QSITE) board planning day by Dr Jason Zagami 25 February 2012 held at QUT
The document provides an overview of the computing curriculum in England, including aims, key stages, and learning objectives. At key stage 1, students will learn about algorithms, basic programming, data storage and retrieval, and online safety. At key stage 2, they will design and write programs, use logical reasoning, understand computer networks and the internet, use search engines effectively, and collect/analyze data. At key stage 3, topics include computational modeling, algorithms, programming languages, Boolean logic, computer systems, and data representation.
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
This document outlines considerations for developing a scheme of work for the new UK computing curriculum. It provides an overview of the curriculum aims and expectations at different key stages, focusing on core concepts like computational thinking, digital literacy and computer science. It also discusses important themes, assessment approaches, and the balance between skills, knowledge and understanding. The document concludes by reflecting on key decisions needed to structure the scheme of work, including topics, format and ensuring it builds on children's interests in learning.
Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from data. It unifies statistics, data analysis, machine learning and related methods. Data science is the future of artificial intelligence and can add value to businesses by turning ideas seen in movies into reality. It involves working with large data sets and machine learning. Data science is primarily used for decisions, predictions, and machine learning by uncovering findings from data. Data science and technology delivers methods for solving data-intensive problems ranging from research to software deployment. Feature engineering is selecting or generating useful columns for modeling. Data cleaning takes up most of a data scientist's time along with exploratory analysis, visualization, machine learning, and communication. Data science education
Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.
SCSA's WA curriculum differs slightly from ACARA's and the new Digital Technologies subject warrants examination. With a secondary focus on coding and computational thinking, this slideshow was used at WA schools to assist in unpacking these components.
Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov
The document provides an overview of the Digital Technologies curriculum in Australia to demystify it for teachers. It discusses how a digital economic future is inevitable and schools need to prepare students with skills like being entrepreneurial, adaptive to change, and digitally discerning. The curriculum focuses on developing skills in areas like computational thinking, design thinking, data representation, and digital systems. It differentiates Digital Technologies, which teaches specific computer science concepts, from general ICT capability. It provides examples of what ICT capability and computational thinking look like at different year levels. The goal is to provide practical opportunities for students to develop innovative solutions through design thinking and information systems knowledge.
Significant Role of Statistics in Computational SciencesEditor IJCATR
This paper is focused on the issues related to optimizing statistical approaches in the emerging fields of Computer Science
and Information Technology. More emphasis has been given on the role of statistical techniques in modern data mining. Statistics is
the science of learning from data and of measuring, controlling, and communicating uncertainty. Statistical approaches can play a vital
role for providing significance contribution in the field of software engineering, neural network, data mining, bioinformatics and other
allied fields. Statistical techniques not only helps make scientific models but it quantifies the reliability, reproducibility and general
uncertainty associated with these models. In the current scenario, large amount of data is automatically recorded with computers and
managed with the data base management systems (DBMS) for storage and fast retrieval purpose. The practice of examining large preexisting
databases in order to generate new information is known as data mining. Presently, data mining has attracted substantial
attention in the research and commercial arena which involves applications of a variety of statistical techniques. Twenty years ago
mostly data was collected manually and the data set was in simple form but in present time, there have been considerable changes in
the nature of data. Statistical techniques and computer applications can be utilized to obtain maximum information with the fewest
possible measurements to reduce the cost of data collection.
This presentation discusses futuristic knowledge management. It introduces knowledge engineering as eliciting, structuring, formalizing, and operationalizing information and knowledge from problem domains to build programs. Some challenges include complex information that is difficult to observe from multiple representations like textbooks and heuristics. Knowledge bases and databases share common principles, and knowledge engineering stimulates exchange between these related fields. The major aim of the journal is to identify, analyze, and publish research on principles of data and knowledge engineering systems. It covers topics like representation, construction, applications, and communication aspects of knowledge-based systems. Emerging areas of computation theory include biological computation, complexity theory, and symbolic computation. Data structuring requires understanding arrangement in memory, algorithms, and performance
The Journal of Computer Engineering and Information Technology (JCEIT) promotes rigorous research that makes a significant contribution in advancing knowledge for computer sciences. JCEIT includes all major themes pertaining to computing and information technology.
Huge amount of data is being collected everywhere - when we browse the web, go to the doctor's clinic, visit the supermarket, tweet or watch a movie. This plethora of data is dealt under a new realm called Data Science. Data Science is now recognized as a highly-critical growing area with impact across many sectors including science, government, finance, health care, social networks, manufacturing, advertising, retail,
and others. This colloquium will try to provide an overview as well as clarify bits and bats about this emerging field.
This document provides an introduction to data science. It discusses that data science uses computer science, statistics, machine learning, visualization, and human-computer interaction to collect, clean, analyze, visualize, and interact with data to create data products. It also describes the data science lifecycle as involving discovery, data preparation, model planning, model building, operationalizing models, and communicating results. Finally, it lists some common tools used in data science like Python, R, SQL, and Tableau.
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
4) Modelling creates algorithms and models by training them on large datasets to perform tasks intelligently.
5) Evaluation tests the project by comparing outputs to actual answers to identify areas for improvement.
Similar to Digital technologies 2015 eq workshop (20)
Zagami, J. (2016, October). Digital Solutions Response. Presentation at the accessIT - ACS Qld State Conference 2016, Brisbane, Australia. Retrieved from http://www.slideshare.net/j.zagami/digital-solutions-response
This document discusses moonshot projects, xThinking labs, and inquiry-based project-based learning (iPBL) led by Dr. Jason Zagami of Griffith University. Dr. Zagami's email and website are provided for further contact.
Zagami, J. & Becker, S. (2016, September). ACCE Leadership Forum Summary. Presentation at the Australian Council for Computers in Education Conference, Brisbane, Australia.
Zagami, J. & Becker, S. (2016, September). ACCE Leadership Forum. Forum conducted at the Australian Council for Computers in Education Conference, Brisbane, Australia.
Three key trends are discussed in the document:
1. Redesigning learning spaces to be more hands-on and support new models like flipped classrooms. Wireless bandwidth and large displays are being upgraded.
2. Rethinking how schools work by making them more flexible, project-based, and multidisciplinary to prepare students for the real world.
3. Increasing collaborative learning both in person and online to improve engagement and allow global collaboration between students and teachers.
Horizon Report K12: What are the trends, challenges and developments in techn...Jason Zagami
Zagami, J. (2016, June) Horizon Report K12: What are the trends, challenges and developments in technology. Keynote presentation presented to Digital Technologies Summit 2016: Initial Teacher Education, Brisbane, Queensland, Australia. https://www.griffith.edu.au/conference/digital-technologies-summit-2016
This document discusses teaching design thinking, computational thinking, systems thinking, strategic thinking, and futures thinking through challenge-based learning. It outlines approaches like the Stanford d.school design process and Daylight Design Thinking process. Key aspects covered include organizing learning environments, contextualizing challenges, the design process, solution types, assessments, expectations for students, and sample contexts in engineering, food production, and materials technologies. Competitions and 2-4 activities/projects are suggested to teach these various thinking approaches.
This document provides an overview of teaching design technologies. It discusses key concepts like systems thinking, design thinking, and contexts. Engineering principles and systems, food and fibre production, food specializations, and materials technologies are presented as contexts. The design process of investigating problems, generating solutions, producing solutions, evaluating solutions, and collaborating is explained. Types of designed solutions like products, services, and environments are also summarized. Overall, the document outlines the main approaches and concepts used for teaching design technologies.
This document outlines a university course on teaching technologies education. It discusses key topics like what technology and educational technologies are, and introduces the technologies learning area. The course covers teaching digital technologies, design technologies, and systems, futures, and strategic thinking over 10 weeks. Students complete a log of learning activities and portfolio of their work which is due at the end. Tutorials involve exploring the Australian curriculum and hands-on challenges in design and programming.
Trends, challenges and developments in technologies that will influence the f...Jason Zagami
Keynote presentation by Dr Jason Zagami to the ASLA conference on 29 September 2015 at Brisbane, Queensland.
Zagami, J. (2015, September) Trends, challenges and developments in technologies that will influence the future of libraries. Keynote presentation presented to ASLA conference, Brisbane, Queensland, Australia. http://www.slideshare.net/j.zagami/trends-challenges-and-developments-in-technologies-that-will-influence-the-future-of-libraries
Teaching the Technologies learning area using a thinking skills approachJason Zagami
Presentation by Dr Jason Zagami to the QSITE2015 conference on 24 September 2015 at Townsville, Queensland.
Zagami, J. (2015, September) Teaching the Technologies learning area using a thinking skills approach. Presentation presented to QSITE2015 conference, Townsville, Queensland, Australia. http://www.slideshare.net/j.zagami/teaching-the-technologies-learning-area-using-a-thinking-skills-approach
The Technologies learning area provides an opportunity to develop in students five distinct but complementary ways of thinking about and understanding the world: Systems Thinking, Design Thinking, Computational Thinking, Futures Thinking, and Strategic Thinking. This session will explore approaches to teaching the Technologies learning area through problem-solving activities that develop these thinking approaches.
The document discusses key concepts in systems thinking. It explains that systems thinking views phenomena holistically by considering large numbers of interactions, rather than isolating smaller parts. Mental models are used to understand complex systems, and dynamic models with stocks, flows, and feedback loops can simulate how systems change over time. Several examples are given to illustrate systems thinking concepts like balancing and reinforcing feedback, and how systems can be viewed from different perspectives.
Developing a Preferred Futures perspectiveJason Zagami
The document discusses developing a preferred futures perspective in technologies education. It aims to conceptualize more just and sustainable human and planetary futures by developing knowledge and skills in exploring probable and preferred futures scenarios. Students learn to understand the dynamics of human, social, and ecological systems and their influence on alternative futures, while also developing a sense of responsibility and action toward creating better futures through techniques like trend analysis, environmental scanning, visioning, and backcasting.
This document discusses creativity, failure, and innovation in technology education. It provides information about how students at different primary school levels (early, middle, upper) approach design tasks and develop their design thinking. For early primary students, design processes are flexible and initial designs may differ significantly from final products. Middle primary students recognize processes used and how they could be improved. They draw on resources to inform design. Upper primary students identify issues and research alternative designs. The document also covers models of the creative process, techniques to inspire creativity like brainstorming, and how innovation involves new solutions rather than just improvements. Failure is presented as an opportunity to learn.
The document discusses teaching technologies education and pedagogical diversity. It covers organizing learning environments, design challenges, contextualizing, personalizing, localizing, and modernizing learning. It also addresses assessing student achievement, cooperative learning models, persistence, unit planning, and common unit planning problems. Key aspects of design thinking are defined, including investigating problems, generating designs, producing solutions, evaluating, and collaborating in an iterative process.
This document discusses several thinking approaches that can be applied to education including design thinking, systems thinking, computational thinking, futures thinking, and strategic thinking. It notes some of the big global problems they could help address such as global warming, food scarcity, and health issues. It also provides an overview of design thinking processes, challenge-based learning approaches, and integrating curriculum into classroom projects and competitions.
This document discusses design thinking and the design process in technologies education. It defines design thinking as using strategies to understand problems, generate creative ideas, and evaluate solutions. It outlines key concepts like contexts, design briefs, and types of designed solutions (products, services, environments). The design process involves investigating problems, generating solutions, producing a solution, evaluating it, and collaborating. Each step of the process is explained in more detail. The document also discusses engineering, food/fiber production, food specializations, and materials/technologies as contexts for design projects.
This document provides an overview of technologies as a learning area in education. It discusses key concepts like computational thinking, design thinking, and futures thinking. It outlines the structure of the technologies curriculum from foundation to year 10, with indicative time allocations. The technologies learning area has two subjects: design and technologies, and digital technologies. The course overview shows a nine-week plan addressing topics like systems thinking and creativity. Assessment includes quizzes, activities, and a portfolio to demonstrate learning in the technologies subjects.
Opportunities games provide for creativity, production, and innovationJason Zagami
Games provide opportunities for creativity, production, and innovation according to Dr. Jason Zagami of Griffith University. Games can be contexts for developing skills in rapid problem solving, risk taking, and simulation. They also allow for production through fan fiction, cosplay, and game making. Games promote innovation for students, teachers, and curriculum development when used in education. Zagami analyzes how the SAMR and TPACK models apply to creativity, production, and innovation through game play and game building in learning contexts.
Secondary Worlds and Computer Gaming in EducationJason Zagami
Zagami, J. (2014, October). Secondary Worlds and computer gaming in Education. Paper presented at the Australian Council for Computers in Education Conference, Adelaide, Australia. Retrieved from http://acec2014.acce.edu.au/sites/2014/files/attachments/ACEC2014%20Secondary%20Worlds%20and%20computer%20gaming%20in%20Education.docx
Fantasy worlds have long enthralled and engaged our imaginations with Tolkien defining those of sufficient detail as Secondary Worlds, distinct from the Primary World of our everyday experience. Within such worlds we can imaginatively explore beyond the narratives provided us and by combining such worlds with the interactivity of games, particularly computer games, extending this ability to explore persistent Secondary Worlds that we can influence and change, share experiences with others, and contribute to the mythologies of these worlds. This rich exploration provides opportunities to learn by enhancing the mental models constructed by our explorations of Secondary Worlds and transferring this learning to the mental models held of similar concepts in the Primary World. Two case studies are briefly detailed to clarify the concepts presented, firstly the use of a Year 8 Social Studies simulation of the world of StatecraftX in which empire building, resource management, and refugee dilemmas provided a context for student engagement with a Secondary World and transfer concepts developed in world to those under study; and secondly, the use of the Secondary world of the Simpsons, particularly the Springfield Primary School, as a familiar Secondary World setting in which to explore teacher education situations and transfer learning to real world practice.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
20. UK dis-application
ICT as a subject name carries negative
connotations of a dated and unchallenging
curriculum that does not serve the needs and
ambitions of pupils. Changing the subject name
of ICT to computing will not only improve the
status of the subject but also more accurately
reflect the breadth of content included in the
proposed new programmes of study
Education secretary Michael Gove MP
January 2012
21. I remember being at school and using early
computers. Yes, I was in computer club - and I
loved it. I think we’ve lost some of that sense of
joy and excitement in computing, and have just
become focused on just training kids to use
Windows. We want to bring some of that
excitement back.
September 2013
Elizabeth Truss
Parliamentary
Under Secretary
of State for Education and Childcare
22. Coding - one of the essential skills of the 21st
century - will now start at age 5. We are aiming to
develop one of the most rigorous computing
curricula in the world, where pupils will learn to
handle detailed, abstract computing processes
and over-11s will learn 2 programming
languages (one of which must be textual).
September 2013
Elizabeth Truss
Parliamentary
Under Secretary
of State for Education and Childcare
24. AIMS
Design, create, manage and evaluate sustainable and innovative
digital solutions to meet and redefine current and future needs
Use computational thinking and the key concepts of abstraction;
data collection, representation and interpretation; specification,
algorithms and implementation to create digital solutions
Confidently use digital systems to efficiently and effectively
automate the transformation of data into information and to
creatively communicate ideas in a range of settings
Apply protocols and legal practices that support safe, ethical and
respectful communications and collaboration with known and
unknown audiences
Apply systems thinking to monitor, analyse, predict and shape the
interactions within and between information systems and the
impact of these systems on individuals, societies, economies and
environments
28. Processes and
production skills
Collecting, managing and analysing data /
Creating digital solutions by:
defining
designing
implementing
evaluating
collaborating and managing
29. Collecting, managing and analysing data, which involves the nature
and properties of data, how they are collected and interpreted
using a range of digital systems and peripheral devices and
interpreting data when creating information
Defining problems and designing digital solutions (Foundation –
Year 2), which develops into defining problems and designing,
implementing and evaluating solutions that have been developed
by students, and evaluating how well existing information systems
meet different needs (Year 3 – 10)
Communicating ideas and information (Foundation – Year 4), which
develops into managing, creating and communicating ideas and
information (Year 5 – 6) through to independently and
collaboratively managing projects to create interactive solutions
(Year 7 – 10). This involves creating and communicating
information, especially online by creating websites, and interacting
safely using appropriate technical and social protocols.
30. Knowledge and
understanding
Digital systems
the components of digital systems:
hardware, software and networks and their use
Representation of data
how data are represented and structured
symbolically
31. Abstraction
Data Collection, Data Representation and Data
Interpretation
Specification, Algorithms and Implementation
Digital Systems
Interactions and Impacts
32. Abstraction
which underpins all content, particularly the
content descriptions relating to the concepts
of data representation and specification,
algorithms and implementation
33. Computational
Thinking
which underpins all content, particularly the
content descriptions relating to the concepts
of data representation and specification,
algorithms and implementation
53. Computational Fairy Tales
The Ant and the Grasshopper: A Fable of Algorithms (Algorithms)
Bullies, Bubble Sort, and Soccer Tickets (Bubble Sort)
Hunting Dragons with Binary Search (Binary Search)
Binary Searching for Cinderella (Binary Search)
Goldilocks and the Two Boolean Bears (Boolean)
The Tortoise, the Hare, and 50000 Ants (Parallel Algorithms)
54. Computational Fairy Tales
The ant paused for a moment while he thought. "It is the algorithm
that we use," he finally replied.
"Algorithm?" asked the grasshopper.
"A set of steps or instructions for accomplishing a task," explained
the ant. "Like when a carpenter builds a chair, he uses an algorithm
that includes measuring, cutting, smoothing, and hammering."
"What task does your algorithm solve?" asked the grasshopper.
"Does it solve the problem of having too much time during the
summer?" He chuckled out loud at his own joke.
63. Computational Thinking
"Computational thinking is a
fundamental skill for everyone, not just
for computer scientists. To reading,
writing, and arithmetic, we should add
computational thinking to every child’s
analytical ability."
Jannette Wing
74. Contexts Computation
Thinking
Key Concepts Possible Units Outcomes
Addressed
Thinking
Perspectives
Information
systems
Analyzing and
logically
organizing data
Data collection (properties,
sources and collection of data),
data representation (symbolism
and separation) and data
interpretation (patterns and
contexts)
Dynamic (database driven)
disaster awareness
website for local
community
Collecting , managing and
analysing data
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Information, Disaster
Response, Natural,
Communication)
Design (Information is designed)
Futures (What might occur and be
needed)
Information
technology
Data modeling,
data abstractions,
and simulations
Digital systems (hardware,
software, and networks and the
internet)
Setting up and managing
an isolated network with
file serving, webserver and
email
Digital systems
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Computer, Software,
Network, Communication)
Design (Networks are designed)
Futures (Personal networks of the
future)
Projects
Computer
science
Formulating
problems such
that computers
may assist
Abstraction, which underpins all
content, particularly the content
descriptions relating to the
concepts of data representation
and specification, algorithms and
implementation
Encryption / Decryption
software system
Representation of data
Collecting , managing and
analysing data
Defining
Designing
Implementing
Evaluating
Systems (Security Communication)
Design (Communication is
designed)
Futures (Cracking technologies)
Projects
Software
engineering
Automating
solutions via
algorithmic
thinking
Specification (descriptions and
techniques), algorithms (following
and describing) and
implementation (translating and
programming)
Game or Mobile App
development
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Software, Usability)
Design (Software is designed)
Futures (Changes in games / apps,
what might come next)
Projects
Computer
engineering
Identifying,
testing, and
implementing
possible solutions
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Robotic / Automation
solution to a problem,
Artificial Intelligence
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Automation systems, AI
systems)
Design (Automation is designed)
Futures (What jobs will disappear
to automation)
Projects
Computer
engineering
Generalizing and
applying a
process to other
problems
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Taking an existing digital or
non digital solution to a
problem and creating a
new and improved digital
solution to that problem
Digital systems
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Problems as a conflict in
systems)
Design (Problems can be
addressed through good design)
Futures (What problems need
solutions)
76. Contexts
Computation Thinking
Information
systems
Analysing and logically organising data
Information
technology
Data modeling, data abstractions, and simulations
Computer
science
Formulating problems such that computers may assist
Software
engineering
Automating solutions via algorithmic thinking
Computer
engineering
Identifying, testing, and implementing possible solutions
Computer
engineering
Generalizing and applying a process to other problems
77. Contexts Computation
Thinking Key Concepts
Information
systems
Analysing and
logically
organising data
Data collection (properties, sources and collection of data), data
representation (symbolism and separation) and data
interpretation (patterns and contexts)
Information
technology
Data modeling,
data abstractions,
and simulations
Digital systems (hardware, software, and networks and the
internet)
Computer
science
Formulating
problems such
that computers
may assist
Abstraction, which underpins all content, particularly the
content descriptions relating to the concepts of data
representation and specification, algorithms and
implementationSoftware
engineering
Automating
solutions via
algorithmic
thinking
Specification (descriptions and techniques), algorithms
(following and describing) and implementation (translating and
programming)
Computer
engineering
Identifying,
testing, and
implementing
possible solutions
Interactions (people and digital systems, data and processes)
and impacts (sustainability and empowerment)
Computer
engineering
Generalizing and
applying a
process to other
problems
Interactions (people and digital systems, data and processes)
and impacts (sustainability and empowerment)
78. Contexts Computation
Thinking
Key Concepts
Possible Units
Information
systems
Analysing and
logically
organising data
Data collection (properties,
sources and collection of data),
data representation (symbolism
and separation) and data
interpretation (patterns and
contexts)
Dynamic (database driven) disaster awareness
website for local community
Information
technology
Data modeling,
data abstractions,
and simulations
Digital systems (hardware,
software, and networks and the
internet)
Setting up and managing an isolated network with
file serving, webserver and email
Computer
science
Formulating
problems such
that computers
may assist
Abstraction, which underpins all
content, particularly the content
descriptions relating to the
concepts of data representation
and specification, algorithms and
implementation
Encryption / Decryption software system
Software
engineering
Automating
solutions via
algorithmic
thinking
Specification (descriptions and
techniques), algorithms (following
and describing) and
implementation (translating and
programming)
Game or Mobile App development
Computer
engineering
Identifying,
testing, and
implementing
possible solutions
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Robotic / Automation solution to a problem,
Artificial Intelligence
Computer
engineering
Generalizing and
applying a
process to other
problems
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Taking an existing digital or non digital solution to
a problem and creating a new and improved digital
solution to that problem
79. Contexts Computation
Thinking
Key Concepts
Outcomes Addressed
Information
systems
Analysing and
logically
organising data
Data collection (properties,
sources and collection of data),
data representation (symbolism
and separation) and data
interpretation (patterns and
contexts)
Collecting , managing and analysing data
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Information
technology
Data modeling,
data abstractions,
and simulations
Digital systems (hardware,
software, and networks and the
internet)
Digital systems
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Computer
science
Formulating
problems such
that computers
may assist
Abstraction, which underpins all
content, particularly the content
descriptions relating to the
concepts of data representation
and specification, algorithms and
implementation
Representation of data
Collecting , managing and analysing data
Defining
Designing
Implementing
Evaluating
Software
engineering
Automating
solutions via
algorithmic
thinking
Specification (descriptions and
techniques), algorithms (following
and describing) and
implementation (translating and
programming)
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Computer
engineering
Identifying,
testing, and
implementing
possible solutions
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Computer
engineering
Generalizing and
applying a
process to other
problems
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Digital systems
Defining
Designing
Implementing
Evaluating
Collaborating and managing
80. Contexts Computation
Thinking Thinking Perspectives
Information
systems
Analysing and
logically
organising data
Systems (Information, Disaster Response, Natural, Communication)
Design (Information is designed)
Futures (What might occur and be needed)
Projects
Information
technology
Data modeling,
data abstractions,
and simulations
Systems (Computer, Software, Network, Communication)
Design (Networks are designed)
Futures (Personal networks of the future)
Projects
Computer
science
Formulating
problems such
that computers
may assist
Systems (Security Communication)
Design (Communication is designed)
Futures (Cracking technologies)
Projects
Software
engineering
Automating
solutions via
algorithmic
thinking
Systems (Software, Usability)
Design (Software is designed)
Futures (Changes in games / apps, what might come next)
Projects
Computer
engineering
Identifying,
testing, and
implementing
possible solutions
Systems (Automation systems, AI systems)
Design (Automation is designed)
Futures (What jobs will disappear to automation)
Projects
Computer
engineering
Generalizing and
applying a
process to other
problems
Systems (Problems as a conflict in systems)
Design (Problems can be addressed through good design)
Futures (What problems need solutions)
Projects
82. Contexts Computation
Thinking
Key Concepts Possible Units Outcomes
Addressed
Thinking
Perspectives
Information
systems
Analysing and
logically
organising data
Data collection (properties,
sources and collection of data),
data representation (symbolism
and separation) and data
interpretation (patterns and
contexts)
Dynamic (database driven)
disaster awareness
website for local
community
Collecting , managing and
analysing data
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Information, Disaster
Response, Natural,
Communication)
Design (Information is designed)
Futures (What might occur and be
needed)
Information
technology
Data modeling,
data abstractions,
and simulations
Digital systems (hardware,
software, and networks and the
internet)
Setting up and managing
an isolated network with
file serving, webserver and
email
Digital systems
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Computer, Software,
Network, Communication)
Design (Networks are designed)
Futures (Personal networks of the
future)
Projects
Computer
science
Formulating
problems such
that computers
may assist
Abstraction, which underpins all
content, particularly the content
descriptions relating to the
concepts of data representation
and specification, algorithms and
implementation
Encryption / Decryption
software system
Representation of data
Collecting , managing and
analysing data
Defining
Designing
Implementing
Evaluating
Systems (Security Communication)
Design (Communication is
designed)
Futures (Cracking technologies)
Projects
Software
engineering
Automating
solutions via
algorithmic
thinking
Specification (descriptions and
techniques), algorithms (following
and describing) and
implementation (translating and
programming)
Game or Mobile App
development
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Software, Usability)
Design (Software is designed)
Futures (Changes in games / apps,
what might come next)
Projects
Computer
engineering
Identifying,
testing, and
implementing
possible solutions
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Robotic / Automation
solution to a problem,
Artificial Intelligence
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Automation systems, AI
systems)
Design (Automation is designed)
Futures (What jobs will disappear
to automation)
Projects
Computer
engineering
Generalizing and
applying a
process to other
problems
Interactions (people and digital
systems, data and processes)
and impacts (sustainability and
empowerment)
Taking an existing digital or
non digital solution to a
problem and creating a
new and improved digital
solution to that problem
Digital systems
Defining
Designing
Implementing
Evaluating
Collaborating and managing
Systems (Problems as a conflict in
systems)
Design (Problems can be
addressed through good design)
Futures (What problems need
solutions)